Measuring functional outcome in upper extremity soft-tissue sarcoma: Validation of the Toronto extremity salvage score and the QuickDASH patient-reported outcome instruments
Bibliographic record
Abstract
Interest in functional outcome (FO) and health-related quality of life (HRQL) in extremity soft-tissue sarcoma (STS) patients has increased. The aim of this study was to validate two FO questionnaires for upper extremity STS patients: the Toronto Extremity Salvage Score (TESS) and short version of the Disability of Arm, Shoulder and Hand (QuickDASH), based on Finnish population data. A multi-center study was conducted at two academic sarcoma centers. Surgically treated upper extremity STS patients were invited to participate. Patients completed the TESS and the QuickDASH with HRQL questionnaires the 15D and the QLQ-C30. The scores were analyzed and compared. Fifty-five patients with a mean follow-up period of 4.7 years were included. Mean age was 63 years (standard deviation [SD] 14.6). The mean score for TESS was 88.5 (SD 15.1) and for QuickDASH 17.8 (SD 19.6). The QuickDASH had a statistically significantly better score coverage. A ceiling effect was noted, 27% and 20% for TESS and QuickDASH, respectively. The TESS and QuickDASH scores were strongly correlated (r= -0.89). The TESS score strongly correlated with the QLQ-C30 (r = 0.79) and the 15D score (r = 0.70). The QuickDASH score correlated strongly with the QLQ-C30 score (r=-0.71) and moderately with the 15D score (r= -0.56). The TESS score had a statistically significantly stronger correlation with the 15D score than QuickDASH (p<0.005). Both the TESS and the QuickDASH provide reliable scores for assessing FO in upper extremity STS patients. The QuickDASH has a better coverage, whereas TESS showed a stronger correlation to HRQL scores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".